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DivOPD, a simple learner-side batch-selection method that spreads a fixed turn budget across more rollouts and, within each rollout, prioritizes turns with larger cumulative teacher-student disagreement, is introduced.
Remarkably, despite operating entirely without pre-loaded metadata, the proposed TRUST-SQL framework consistently matches or surpasses strong baselines that rely on schema prefilling.
Adaptive Multi-Reward Projection is proposed, a lightweight online method that reallocates aggregation weights using three signals, relative shortfall, reward volatility, and recent progress, increasing pressure on lagging, unstable, or stagnant dimensions while relieving saturated ones.
CARE is proposed, which grounds every rubric evolution step in a high-quality anchor response generated by a frontier model conditioned on the prompt and its rubrics, enabling two complementary mechanisms: an Adaptive branch that reactively repairs reward misspecification; and a Chase branch that proactively converts f...
SelSkill is proposed, a dual-granularity preference-learning framework for selective skill invocation that formulates skill use as a skill-or-skip decision, uses predictive uncertainty to prioritize candidate decision points, and constructs controlled invoke-skip preference pairs from shared trajectory prefixes.
AOPD replaces ineffective negative reinforcement with localized divergence minimization in non-positive advantage regions while preserving positive reinforcement learning and maintains higher policy entropy during training and better capability retention during sequential tool-use adaptation.
Experimental results demonstrate that the ToolVerse framework significantly strengthens LLMs'capabilities in long-horizon tool use, achieving a marked performance boost and showcasing robust reasoning within dynamic environments.
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